Typical business intelligence tools work by receiving and storing large quantities of business data (such as details of daily operations). While the tools are capable of outputting the information in pre-determined report formats and dimensional views (that show, e.g., the number of products sold in a quarter), a human analyst is required to sift through and interpret the significance of the information. Determining which information is of interest is a manual process—the analyst will look through what may be potentially hundreds of charts or other voluminous information to determine, for example, that discounting particular products results in more revenue than discounting other products.
One reason for this problem is that while existing business intelligence tools are capable of aggregating large amounts of information, interpreting the measured quantities (referred to herein as “metrics”) in context remains a problem. For example, existing tools may be able to determine whether the total number of a product A sold this quarter is greater or less than the number sold last quarter and whether the average selling price of product B is higher this quarter or last quarter. However, it is difficult to discern whether the changes in those values are meaningful (such as from a revenue standpoint), whether efforts spent selling those products ought to have been focused elsewhere, and what the optimal value for those two metrics, taken together, ought to be, etc. It would be useful to provide more relevant comparisons.
In some cases, companies use dashboards or other visual tools to convey information selected by the analyst to employees. Suppose a firm has 2000 employees and an analyst determines that two charts are of particular interest—the number of a particular product sold last month, and the average selling price of that product. While the particular values shown to each employee may vary (e.g., showing Bob that he sold 100 units at an average of $97 and Jane that she sold 90 units at an average of $100), when the 2000 employees visit their respective dashboards, they will each be presented with those two specific charts, irrespective of whether those charts are meaningful or helpful to the individual employee. It would therefore be desirable to have improved methods of conveying information.